Elsevier

Social Science & Medicine

Volume 53, Issue 12, December 2001, Pages 1587-1597
Social Science & Medicine

An investigation into the cyclical incidence of dengue fever

https://doi.org/10.1016/S0277-9536(00)00443-3Get rights and content

Abstract

The purpose of this research was to review the topic of dengue fever transmission and investigate the relationship between seasonal temperature fluctuations and cyclical dengue fever incidence. Data from Puerto Rico (1988–1992) were used to test the model proposed. Dengue fever is a viral disease caused by any one of four antigenically distinct serotypes. It is transmitted by Aedes mosquitoes and infects 80 million people per year. Currently, dengue is endemic in specific tropical and subtropical regions worldwide and epidemic dengue has been reported in the Americas, Asia and some Pacific Islands. Data for Puerto Rico were collected from the NCDC/NOAA and a study conducted by Perez et al. (1994). Multivariate linear regression analysis was used to determine if a relationship exists between the monthly mean temperature lagged and the monthly incidence of dengue fever in Puerto Rico. Statistical significance was achieved and a second-order model produced an R2 of 0.71. A residual analysis reveals positive autocorrelation, thus weakening the model's power to predict monthly dengue incidence. This suggests that other forces or factors related to the history of the herd immunity, the introduction of a new serotype, or demographic transitions are also influencing the cyclical transmission of dengue fever. Case clustering information, regional dengue distributions, and population density transformations must also be obtained in order to assess the forecasting ability of this model. Additional research is needed to avoid oversimplifying the problem. Without such attempts at establishing significant correlations, dengue prevention and control will remain a formidable task for many developing and developed countries.

Introduction

Demographic changes such as rapid, unplanned rural–urban migration and increasing population in areas lacking adequate infrastructure play a major role in disease transmission and the spread of harmful pathogens into previously unaffected areas. Socioeconomic status and human settlement patterns also affect human susceptibility to vector-borne diseases. For example, a population said to be at risk for a specific mosquito-borne disease might experience relatively low levels of infection if preventative measures such as screens, insect repellants or vaccinations are available and affordable. Furthermore, impoverished populations often find it necessary to migrate in order to find work. This migration may facilitate the introduction and dissemination of new infectious agents into a community, in which no previous immunity has been acquired (Monath, 1995; Gubler & Kuno, 1997). Likewise, populations settled in isolated areas may not have access to timely treatment; thus, increasing the morbidity/mortality rate associated with specific infectious diseases.

Temperature and precipitation fluctuations also significantly affect the introduction and dissemination of harmful pathogens affecting human populations today (Patz, Epstein, Burke, & Balbus, 1996). Temperature changes affect vector-borne disease transmission and epidemic potential by altering the vector's reproductive or biting rate; by shifting a vector's geographic range or distribution; by altering the extrinsic incubation period of the pathogen; and, by increasing or decreasing vector–pathogen–host interaction and thereby affecting host susceptibility (WHO, 1998; Gratz, 1999). Precipitation is of importance in vector-borne disease transmission because of its affect on adult female mosquito density. An increase in rainfall leads to an increase in available breeding sites which in turn leads to an increase in the number of mosquitoes. An increase in the number of adult female mosquitoes increases the odds of a mosquito acquiring a pathogen and transmitting it to a second susceptible host (Gubler & Kuno, 1997). Because insects and ticks thrive in warm, moist climates and have the ability to exploit newly disturbed ecosystems, tropical and subtropical regions experiencing high levels of urbanization and increased deforestation are often the areas at the greatest risk for vector-borne disease epidemics.

The purpose of this study is to investigate the relationship between seasonal temperature fluctuations and the cyclical incidence of dengue fever using multivariate regression analysis. Because the effect of rainfall on female mosquito density is not the same for all dengue vectors, and because breeding site preferences vary (Jetten & Focks, 1997), establishing a universal relationship between mosquito density, dengue incidence, and precipitation is extremely complex and is therefore beyond the scope of this analysis.1 Puerto Rico was chosen as a test site due to the epidemic nature of dengue fever on the island and the availability of temperature data and previously published monthly dengue data. The resulting empirical model may prove useful in the prediction of future dengue fever incidence and demonstrates the importance of regression analysis as a tool for understanding vector-borne disease ecology.

Section snippets

Global status of dengue fever

Dengue fever/dengue hemorrhagic fever is one disease system endemic in many tropical and subtropical regions and is heavily influenced by environmental conditions, climatic fluctuations and demographic changes in areas that harbor the primary mosquito vectors, Aedes aegypti and Aedes albopictus (Fig. 1). In terms of morbidity and mortality, dengue fever and dengue hemorrhagic fever (DHF)/dengue shock syndrome (DSS) have been called the most important viral diseases transmitted by an arthropod (

Study purpose, data and methods

The objective of this study is to investigate dengue fever incidence in Puerto Rico and determine to what extent regression analysis can be used as a statistical tool for understanding vector-borne disease ecology. This investigation is geared towards the development of an empirical model/equation that accurately reflects the cyclical nature of dengue fever incidence in Puerto Rico.

Temperature data were obtained through the National Climatic Data Center/NOAA Division for the years 1988–1992.

Results and discussion

An initial review of the plotted data indicates that dengue infections reported fluctuate seasonally throughout the year in Puerto Rico (Fig. 2). Fig. 2 illustrates a peak in dengue cases reported between the 9th and 11th month for each year in this case study. A scattergram showing the relationship between temperature and time also reveals seasonal fluctuations in temperature with the highest monthly mean temperatures occurring between the 6th and 9th month for each year in this case study (

Development of empirical model

An alpha level of 0.05 was chosen to indicate the significance of the independent variable (temperature) and statistical strength was sacrificed in the model to avoid losing information contained in the temperature variable and any significant second order variables applied to the model. The null hypothesis (H0) is that temperature lagged 3 months is not a significant factor affecting monthly dengue incidence in Puerto Rico between 1989 and 1992. The alternative hypothesis (Ha) is that

Application of model: understanding the disease ecology of dengue fever

The signs of the residuals produced in this analysis and plotted on Fig. 8 indicate the presence of positive autocorrelation. This suggests other influences may be contributing to the models consistent over-predicting during the first 25 months (with a few exceptions) and its consistent under-predicting for the latter 18 months (again, with one exception). For example, it is possible that the 18 months at the end of the study period were hotter on average than the corresponding first 25 months.

Conclusion

In summary, the presence of residual autocorrelation and inconsistent predicting in peak months suggests that other factors or forces may also be contributing to seasonal dengue incidence in Puerto Rico. Although temperature lagged 3 months behind the monthly incidence of dengue tested significant as an independent variable and correctly models an ecological relationship, it is not an absolute determinant. It is possible that predator–prey relationships, the history of the herd immunity

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